1. ValIdated Systematic IntegratiON: A VISION for epigenomics in hematopoietic gene regulation
Ross Hardison
Department of Biochemistry and Molecular Biology
Huck Institute for Genome Sciences
Penn State University
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2. Mapping function-associated features genome-wide: Sparse
For 2000 cell types, could require ~800 million *-seq assays
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3. Focused efforts of multiple labs on one system gets closer to completeness
HSC
CMP
GMP
MEP
CLP
MEG
ERY
EOS
Mast
GRA
MONO T B NK
Hematopoiesis and datasets
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4. Rationale for the VISION project
Acquisition of genome-wide epigenetic data across hematopoiesis is no longer the major barrier to understanding mechanisms of gene regulation during normal and pathological tissue development
The chief challenges are how to
integrate epigenetic data in terms that are accessible and understandable to a broad community of researchers
build validated quantitative models explaining how the dynamics of gene expression relates to epigenetic features
translate information effectively from mouse models to potential applications in human health.
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5. VISION: ValIdated Systematic IntegratiON of epigenomics in hematopoietic gene regulation
Acquire
Integrate
Validate
Translate
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6. Initial VISION Resources
BX Browser: Visualize functional genomics data
3D Genome Browser
CODEX compendium
of functional genomics
Repository of hematopoietic transcriptomes
Jens Lichtenberg poster
IDEAS data integration
Single cell transcriptomes, HSC
Gottgens lab
ENCODE Element Browser
Translate between mouse and human
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7. Generate, compile, and curate epigenomic data
Work from individual labs
736 datasets
11,774 datasets
High quality,
high information tracks
Hematopoietic cells :
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8. IDEAS to integrate histone modifications and ATAC-seq across cell types
ATAC: Hardison & Bodine, Amit lab
Histone Mod iChIP: Amit lab
IDEAS: Integrative and Discriminative Epigenome Annotation System: 2D segmentation
Yu Zhang et al. 2016
NAR14:
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Promoter
Active chromatin
Quiescent

9. Chromatin interactions for target prediction
Chr Mb, Res=40kb
Promoter Capture HiC. Mifsud et al Nature Genetics
Capture C. Hughes et al Nature Genetics
HiC. Lieberman-Aiden et al Science.
ChIA-PET. Fullwood et al Nature. :
Working on: Target gene assignments for CRMs
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10. Try to integrate all the epigenomic and expression information to derive rules for regulation that apply globally
rules = equations
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11. Modeling different aspects of regulation in VISION
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12. Functional output from distal CRMs measured for Hbb locus
Blood, 2012
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13. Locus model for Hbb and Hba
Locus model: States the functional output Xi,j from each of the cis-regulatory modules (CRMs) contributing to the expression level of the target gene (T).
E.g. here is a formal statement of results from Bender et al. 2012:
THbb = XHS1 + XHS2 + XHS3 + XHS4 + XHS5,6 =
For the Hba complex of enhancers (Hay et al Nature Genetics 48: 898):
THba = XR1 + XR2 + XR3 + XRm + XR4 =
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14. Models for cis-regulatory modules (CRMs)
CRM model: Quantitative estimates of the contribution of epigenomic features, sequence, conservation, etc. to the functional output Xi,j from each of the CRMs
XHS2, Hbb-b1 = 0.41= combination of f(chromatin state), f(TF occupancy), …
XHS1, Hbb-b1 = 0.22
HS1
HS2
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15. Global application of models
Once you have a CRM model, you can apply it globally
It is an equation using variables for which you have measurements genome-wide
H3K27ac, GATA1 occupancy, TAL1 occupancy, motifs, etc.
So you can predict Xi,j for all candidate CRMs
We learned it from a few CRMs in a few loci, and of course it should work there. But what about other loci?
Test these predictions! Genome editing in additional, reference loci
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16. Deliverables from VISION
Comprehensive catalogs of cis-regulatory modules utilized during hematopoiesis
Built by integration of multiple data types
Validated by extensive experimental tests
Quantitative models for gene regulation
Built by machine learning
Extensively tested by genome editing approaches in ten reference loci
Predictions applied genome-wide.
A guide for investigators to translate insights from mouse models to human clinical studies.
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17. Nascent VISION gives new insights
Previous studies:
Autoregulation by GFI1B binding to promoter proximal CRM
Moroy et al NAR 33:987.
Multipotent progenitor cells
Maturing erythroid cells
Structural TFs
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18. Interpreting the maps as testable hypotheses
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19. Thanks to the VISION team
Cheryl Keller
Yu Zhang
Gerd Blobel
James Taylor
Berthold Gottgens
Amber Miller
Feng Yue
Mitch Weiss
David Bodine
Doug Higgs
Belinda Giardine
Jim Hughes
Hardison Lab
Supported by
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